Numpy 2.0 compatibility: proposed release 1.2.2

[jmrohwer]

Now that numpy 2.0 has been released it is time to cut another PySCeS release as all extension modules need to be recompiled against numpy 2.0 to work with numpy>=2.0. The minimum supported numpy version is now 1.23.5 in line with scipy and other downstream projects.

This PR fixes some numpy 2.0 compatibility-related bugs and updates requirements.

In addition:

• Added convenience functions for loading a model directly from and exporting a model directly to SBML, as well as saving as PSC.
• Fixed parallel scanning (requires dill to pickle model object).
• Now that assimulo has finally been ported to Python 3.12, we can finally release Anaconda packages for Python 3.12 (CI build passes).

@bgoli Nothing seems controversial here but I would appreciate your quick glance. I've tested the module with both numpy=2.0.0 and numpy=1.23.5 and all CI builds pass (with their built in tests).

[bgoli]

Hi @jmrohwer, just finishing off the academic year, will dive into the review next week.

[bgoli]

Quick note: I created a new Python10 environment to test the release in and decided to try using RTools 4.4. I followed the PySCeS INSTALL instructions and only had to change the paths to get it to work:

c:\rtools40\usr\bin
c:\rtools40\ucrt64\bin

to

c:\rtools44\usr\bin
c:\rtools44\x86_64-w64-mingw32.static.posix\bin

I haven't tested it extensively yet, and am not even sure if this is the correct way to do it, but it seems to work.

[jmrohwer]

Quick note: I created a new Python10 environment to test the release in and decided to try using RTools 4.4. I followed the PySCeS INSTALL instructions and only had to change the paths to get it to work:

c:\rtools40\usr\bin
c:\rtools40\ucrt64\bin

to

c:\rtools44\usr\bin
c:\rtools44\x86_64-w64-mingw32.static.posix\bin

I haven't tested it extensively yet, and am not even sure if this is the correct way to do it, but it seems to work.

Yes, I've tested it as well and it works fine. However, it is a 428M download compared to 166M for version 4.0, and version 4.0 works equally well. It is also what numpy and scipy uses, so I've oriented myself by that.